Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations308
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.2 KiB
Average record size in memory80.4 B

Variable types

Numeric9
Categorical1

Alerts

diameter is highly overall correlated with height and 7 other fieldsHigh correlation
height is highly overall correlated with diameter and 7 other fieldsHigh correlation
length is highly overall correlated with diameter and 7 other fieldsHigh correlation
rings is highly overall correlated with diameter and 6 other fieldsHigh correlation
sex is highly overall correlated with diameter and 6 other fieldsHigh correlation
shell_weight is highly overall correlated with diameter and 7 other fieldsHigh correlation
shucked_weight is highly overall correlated with diameter and 7 other fieldsHigh correlation
viscera_weight is highly overall correlated with diameter and 7 other fieldsHigh correlation
whole_weight is highly overall correlated with diameter and 7 other fieldsHigh correlation
id has unique values Unique

Reproduction

Analysis started2025-02-02 08:38:35.323798
Analysis finished2025-02-02 08:38:41.101882
Duration5.78 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct308
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.44156
Minimum1
Maximum343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-02-02T14:08:41.177429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.35
Q183.75
median176.5
Q3260.25
95-th percentile326.65
Maximum343
Range342
Interquartile range (IQR)176.5

Descriptive statistics

Standard deviation100.52478
Coefficient of variation (CV)0.57958876
Kurtosis-1.2352299
Mean173.44156
Median Absolute Deviation (MAD)88.5
Skewness-0.039505903
Sum53420
Variance10105.231
MonotonicityNot monotonic
2025-02-02T14:08:41.278476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103 1
 
0.3%
6 1
 
0.3%
210 1
 
0.3%
333 1
 
0.3%
258 1
 
0.3%
294 1
 
0.3%
192 1
 
0.3%
277 1
 
0.3%
161 1
 
0.3%
314 1
 
0.3%
Other values (298) 298
96.8%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
11 1
0.3%
ValueCountFrequency (%)
343 1
0.3%
342 1
0.3%
340 1
0.3%
339 1
0.3%
338 1
0.3%
337 1
0.3%
336 1
0.3%
335 1
0.3%
334 1
0.3%
333 1
0.3%

sex
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
M
126 
F
120 
I
62 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters308
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowI
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 126
40.9%
F 120
39.0%
I 62
20.1%

Length

2025-02-02T14:08:41.367377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:08:41.420655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 126
40.9%
f 120
39.0%
i 62
20.1%

Most occurring characters

ValueCountFrequency (%)
M 126
40.9%
F 120
39.0%
I 62
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 126
40.9%
F 120
39.0%
I 62
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 126
40.9%
F 120
39.0%
I 62
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 126
40.9%
F 120
39.0%
I 62
20.1%

length
Real number (ℝ)

High correlation 

Distinct93
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49125
Minimum0.075
Maximum0.725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-02-02T14:08:41.490001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.075
5-th percentile0.265
Q10.415
median0.515
Q30.58
95-th percentile0.66325
Maximum0.725
Range0.65
Interquartile range (IQR)0.165

Descriptive statistics

Standard deviation0.12571805
Coefficient of variation (CV)0.25591461
Kurtosis0.11939269
Mean0.49125
Median Absolute Deviation (MAD)0.0775
Skewness-0.67601451
Sum151.305
Variance0.015805029
MonotonicityNot monotonic
2025-02-02T14:08:41.584322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.47 10
 
3.2%
0.53 10
 
3.2%
0.565 9
 
2.9%
0.5 9
 
2.9%
0.595 8
 
2.6%
0.45 8
 
2.6%
0.52 7
 
2.3%
0.58 7
 
2.3%
0.63 7
 
2.3%
0.55 7
 
2.3%
Other values (83) 226
73.4%
ValueCountFrequency (%)
0.075 1
0.3%
0.11 1
0.3%
0.16 1
0.3%
0.165 1
0.3%
0.17 1
0.3%
0.175 1
0.3%
0.19 1
0.3%
0.2 1
0.3%
0.205 1
0.3%
0.21 1
0.3%
ValueCountFrequency (%)
0.725 3
1.0%
0.71 2
0.6%
0.705 2
0.6%
0.7 1
 
0.3%
0.695 2
0.6%
0.68 4
1.3%
0.665 2
0.6%
0.66 1
 
0.3%
0.65 2
0.6%
0.645 2
0.6%

diameter
Real number (ℝ)

High correlation 

Distinct82
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38560065
Minimum0.055
Maximum0.575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-02-02T14:08:41.682348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.055
5-th percentile0.19675
Q10.32
median0.405
Q30.46125
95-th percentile0.53325
Maximum0.575
Range0.52
Interquartile range (IQR)0.14125

Descriptive statistics

Standard deviation0.10390524
Coefficient of variation (CV)0.26946335
Kurtosis-0.064123812
Mean0.38560065
Median Absolute Deviation (MAD)0.0675
Skewness-0.62925909
Sum118.765
Variance0.010796299
MonotonicityNot monotonic
2025-02-02T14:08:41.775219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.475 12
 
3.9%
0.45 11
 
3.6%
0.4 10
 
3.2%
0.425 9
 
2.9%
0.355 8
 
2.6%
0.435 8
 
2.6%
0.485 8
 
2.6%
0.44 8
 
2.6%
0.415 8
 
2.6%
0.48 7
 
2.3%
Other values (72) 219
71.1%
ValueCountFrequency (%)
0.055 1
0.3%
0.09 1
0.3%
0.12 2
0.6%
0.13 2
0.6%
0.145 2
0.6%
0.15 2
0.6%
0.16 2
0.6%
0.175 1
0.3%
0.19 1
0.3%
0.195 2
0.6%
ValueCountFrequency (%)
0.575 1
 
0.3%
0.57 2
0.6%
0.56 4
1.3%
0.555 1
 
0.3%
0.55 3
1.0%
0.545 1
 
0.3%
0.54 1
 
0.3%
0.535 3
1.0%
0.53 1
 
0.3%
0.525 3
1.0%

height
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1350487
Minimum0.01
Maximum0.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-02-02T14:08:41.861738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.06175
Q10.105
median0.135
Q30.165
95-th percentile0.2
Maximum0.24
Range0.23
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.041822265
Coefficient of variation (CV)0.30968284
Kurtosis-0.21854854
Mean0.1350487
Median Absolute Deviation (MAD)0.03
Skewness-0.18911997
Sum41.595
Variance0.0017491019
MonotonicityNot monotonic
2025-02-02T14:08:41.953809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.165 18
 
5.8%
0.125 17
 
5.5%
0.16 17
 
5.5%
0.14 16
 
5.2%
0.13 15
 
4.9%
0.175 14
 
4.5%
0.105 14
 
4.5%
0.155 14
 
4.5%
0.12 13
 
4.2%
0.135 13
 
4.2%
Other values (33) 157
51.0%
ValueCountFrequency (%)
0.01 1
 
0.3%
0.03 2
 
0.6%
0.035 1
 
0.3%
0.04 2
 
0.6%
0.045 1
 
0.3%
0.05 1
 
0.3%
0.055 2
 
0.6%
0.06 6
1.9%
0.065 2
 
0.6%
0.07 4
1.3%
ValueCountFrequency (%)
0.24 2
 
0.6%
0.23 1
 
0.3%
0.225 1
 
0.3%
0.22 1
 
0.3%
0.215 3
1.0%
0.21 2
 
0.6%
0.205 3
1.0%
0.2 4
1.3%
0.195 5
1.6%
0.19 7
2.3%

whole_weight
Real number (ℝ)

High correlation 

Distinct289
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73109253
Minimum0.002
Maximum2.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-02-02T14:08:42.044846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.095525
Q10.352625
median0.689
Q31.013
95-th percentile1.617275
Maximum2.55
Range2.548
Interquartile range (IQR)0.660375

Descriptive statistics

Standard deviation0.47602648
Coefficient of variation (CV)0.65111659
Kurtosis0.35618696
Mean0.73109253
Median Absolute Deviation (MAD)0.3305
Skewness0.70395833
Sum225.1765
Variance0.22660121
MonotonicityNot monotonic
2025-02-02T14:08:42.140583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.922 3
 
1.0%
0.5415 3
 
1.0%
0.2255 3
 
1.0%
0.9885 2
 
0.6%
0.042 2
 
0.6%
0.156 2
 
0.6%
0.406 2
 
0.6%
1.013 2
 
0.6%
0.205 2
 
0.6%
1.1015 2
 
0.6%
Other values (279) 285
92.5%
ValueCountFrequency (%)
0.002 1
0.3%
0.008 1
0.3%
0.021 1
0.3%
0.0215 1
0.3%
0.03 1
0.3%
0.0315 1
0.3%
0.037 1
0.3%
0.038 1
0.3%
0.042 2
0.6%
0.0465 1
0.3%
ValueCountFrequency (%)
2.55 1
0.3%
2.141 1
0.3%
2.124 1
0.3%
1.981 1
0.3%
1.959 1
0.3%
1.9565 1
0.3%
1.9485 1
0.3%
1.842 1
0.3%
1.798 1
0.3%
1.779 1
0.3%

shucked_weight
Real number (ℝ)

High correlation 

Distinct283
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28550162
Minimum0.001
Maximum1.0705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-02-02T14:08:42.234638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.039
Q10.140875
median0.274
Q30.394875
95-th percentile0.575825
Maximum1.0705
Range1.0695
Interquartile range (IQR)0.254

Descriptive statistics

Standard deviation0.18112219
Coefficient of variation (CV)0.63439987
Kurtosis0.94632176
Mean0.28550162
Median Absolute Deviation (MAD)0.12575
Skewness0.74133044
Sum87.9345
Variance0.032805248
MonotonicityNot monotonic
2025-02-02T14:08:42.457775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.234 3
 
1.0%
0.32 2
 
0.6%
0.381 2
 
0.6%
0.269 2
 
0.6%
0.0755 2
 
0.6%
0.394 2
 
0.6%
0.097 2
 
0.6%
0.341 2
 
0.6%
0.295 2
 
0.6%
0.063 2
 
0.6%
Other values (273) 287
93.2%
ValueCountFrequency (%)
0.001 1
0.3%
0.0025 1
0.3%
0.007 1
0.3%
0.0075 1
0.3%
0.0105 1
0.3%
0.0125 1
0.3%
0.013 1
0.3%
0.0165 1
0.3%
0.0175 1
0.3%
0.0185 1
0.3%
ValueCountFrequency (%)
1.0705 1
0.3%
0.9455 1
0.3%
0.8175 1
0.3%
0.815 1
0.3%
0.7665 1
0.3%
0.765 1
0.3%
0.7125 1
0.3%
0.65 1
0.3%
0.633 1
0.3%
0.63 1
0.3%

viscera_weight
Real number (ℝ)

High correlation 

Distinct247
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15853409
Minimum0.0005
Maximum0.541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-02-02T14:08:42.552352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.022525
Q10.075375
median0.151
Q30.222875
95-th percentile0.334825
Maximum0.541
Range0.5405
Interquartile range (IQR)0.1475

Descriptive statistics

Standard deviation0.10180579
Coefficient of variation (CV)0.64216968
Kurtosis0.19774282
Mean0.15853409
Median Absolute Deviation (MAD)0.07375
Skewness0.63987731
Sum48.8285
Variance0.010364418
MonotonicityNot monotonic
2025-02-02T14:08:42.649225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.114 3
 
1.0%
0.261 3
 
1.0%
0.2355 3
 
1.0%
0.1125 3
 
1.0%
0.0415 3
 
1.0%
0.227 2
 
0.6%
0.214 2
 
0.6%
0.194 2
 
0.6%
0.1935 2
 
0.6%
0.1625 2
 
0.6%
Other values (237) 283
91.9%
ValueCountFrequency (%)
0.0005 1
0.3%
0.002 1
0.3%
0.0045 1
0.3%
0.005 1
0.3%
0.0065 2
0.6%
0.008 1
0.3%
0.0095 1
0.3%
0.0105 1
0.3%
0.0125 1
0.3%
0.014 1
0.3%
ValueCountFrequency (%)
0.541 1
0.3%
0.483 1
0.3%
0.4515 1
0.3%
0.4115 1
0.3%
0.409 1
0.3%
0.408 1
0.3%
0.398 1
0.3%
0.3925 1
0.3%
0.388 1
0.3%
0.3765 1
0.3%

shell_weight
Real number (ℝ)

High correlation 

Distinct137
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23478247
Minimum0.0015
Maximum1.005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-02-02T14:08:42.743400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.0287
Q10.115
median0.21
Q30.325
95-th percentile0.521
Maximum1.005
Range1.0035
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.16372104
Coefficient of variation (CV)0.69733078
Kurtosis2.4700569
Mean0.23478247
Median Absolute Deviation (MAD)0.1075
Skewness1.2288779
Sum72.313
Variance0.02680458
MonotonicityNot monotonic
2025-02-02T14:08:42.835472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.285 7
 
2.3%
0.15 7
 
2.3%
0.1 7
 
2.3%
0.075 7
 
2.3%
0.26 6
 
1.9%
0.135 6
 
1.9%
0.205 6
 
1.9%
0.325 5
 
1.6%
0.12 5
 
1.6%
0.21 5
 
1.6%
Other values (127) 247
80.2%
ValueCountFrequency (%)
0.0015 1
 
0.3%
0.003 1
 
0.3%
0.005 2
0.6%
0.01 1
 
0.3%
0.011 1
 
0.3%
0.012 1
 
0.3%
0.0125 1
 
0.3%
0.015 4
1.3%
0.02 1
 
0.3%
0.025 1
 
0.3%
ValueCountFrequency (%)
1.005 1
0.3%
0.85 1
0.3%
0.815 1
0.3%
0.78 1
0.3%
0.76 1
0.3%
0.725 1
0.3%
0.69 1
0.3%
0.675 1
0.3%
0.65 1
0.3%
0.62 1
0.3%

rings
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.87987
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-02-02T14:08:42.909246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median10
Q313
95-th percentile19
Maximum26
Range25
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0310502
Coefficient of variation (CV)0.37050536
Kurtosis0.8007122
Mean10.87987
Median Absolute Deviation (MAD)2
Skewness0.7434657
Sum3351
Variance16.249365
MonotonicityNot monotonic
2025-02-02T14:08:42.976783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10 45
14.6%
9 35
11.4%
7 33
10.7%
8 28
9.1%
12 26
8.4%
13 23
7.5%
11 21
6.8%
14 20
 
6.5%
15 16
 
5.2%
5 10
 
3.2%
Other values (13) 51
16.6%
ValueCountFrequency (%)
1 1
 
0.3%
3 2
 
0.6%
4 8
 
2.6%
5 10
 
3.2%
6 6
 
1.9%
7 33
10.7%
8 28
9.1%
9 35
11.4%
10 45
14.6%
11 21
6.8%
ValueCountFrequency (%)
26 1
 
0.3%
23 2
 
0.6%
22 2
 
0.6%
21 3
 
1.0%
20 5
 
1.6%
19 5
 
1.6%
18 5
 
1.6%
17 4
 
1.3%
16 7
2.3%
15 16
5.2%

Interactions

2025-02-02T14:08:40.400300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:35.543654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.137288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.788712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.354337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.018992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.626260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.185388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.737336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.463542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:35.610814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.284969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.851109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.425178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.088297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.691159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.250454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.802257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.525690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:35.682997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.347587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.912283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.492176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.180597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.754697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.312648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.863438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.586150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:35.746284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.409081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.972184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.557651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.243374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.814164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.373339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.031636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.645246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:35.811554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.471445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.034287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.615475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.305835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.875657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.435039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.095408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.705596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:35.875426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.539609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.095207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.677498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.369299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.940434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.493142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.158831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.766592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:35.942211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.601308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.160556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.742676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.437845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.001735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.555822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.218973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.827254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.006290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.664986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.221781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.803918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.500547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.063543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.615333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.280092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.885915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.073448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:36.727063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.289473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:37.959503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:38.562323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.124728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:39.677501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:08:40.339436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-02T14:08:43.038524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
diameterheightidlengthringssexshell_weightshucked_weightviscera_weightwhole_weight
diameter1.0000.922-0.0260.9860.8110.5440.9710.9710.9600.984
height0.9221.0000.0840.9110.8100.5300.9380.9070.9210.938
id-0.0260.0841.000-0.0380.0370.111-0.021-0.035-0.031-0.015
length0.9860.911-0.0381.0000.8020.5280.9650.9680.9630.981
rings0.8110.8100.0370.8021.0000.4740.8460.7920.7860.831
sex0.5440.5300.1110.5280.4741.0000.5010.5220.5400.535
shell_weight0.9710.938-0.0210.9650.8460.5011.0000.9450.9420.983
shucked_weight0.9710.907-0.0350.9680.7920.5220.9451.0000.9670.981
viscera_weight0.9600.921-0.0310.9630.7860.5400.9420.9671.0000.973
whole_weight0.9840.938-0.0150.9810.8310.5350.9830.9810.9731.000

Missing values

2025-02-02T14:08:40.974026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-02T14:08:41.054809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idsexlengthdiameterheightwhole_weightshucked_weightviscera_weightshell_weightrings
06I0.4250.3000.0950.35150.14100.07750.1208
1210I0.3700.2800.0950.26550.12200.05200.0807
2333I0.4750.3650.1150.49900.23200.08850.15610
3111M0.4950.3950.1250.54150.23750.13450.1559
477M0.5950.4750.1400.94400.36250.18900.3159
585M0.5800.4500.1401.01300.38000.21600.36014
64M0.4400.3650.1250.51600.21550.11400.15510
7255M0.6000.4950.1651.24150.48500.27750.34015
8259F0.6350.5050.1701.41500.60500.29700.36515
947M0.4700.3700.1200.57950.29300.22700.1409
idsexlengthdiameterheightwhole_weightshucked_weightviscera_weightshell_weightrings
298331M0.5000.3800.1550.59550.21350.16100.20012
299215F0.4850.3950.1600.66000.24750.12800.23514
300122I0.3850.2950.0850.25350.10300.05750.0857
301343M0.7100.5550.1951.94850.94550.37650.49512
30221M0.3550.2800.0950.24550.09550.06200.07511
303189F0.6300.4800.1601.19900.52650.33500.31511
30472F0.4000.3200.1100.35300.14050.09850.1008
305107F0.5450.4300.1650.80200.29350.18300.28011
306271F0.6400.5250.2151.77900.45350.28550.55022
307103M0.5300.4350.1600.88300.31600.16400.33515